Data visualization - Healy (Chapter 3 Code)
Harro Cyranka
1/2/2019
Loading libraries
library(tidyverse)
library(gapminder)
library(here)Section 3.3: Mappings link data to things you see
- A mapping does not directly say what particular colors or shapes will be on the plot. Rather, it says which variables in the data will be represented by visual elements like a color, shape, or a point on the plot area.
Figure 3.4: A scatterplot of life expectancy vs GDP
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,y =lifeExp))
p + geom_point()
Section 3.4: Build your plots layer by laywer
- ggplot’s main idea is of building up plots a piece at a time in a systematic and predictable fashion, beginning with a mapping between a variable and an aesthetic element.
Figure 3.5: Life expectancy vs GDP, using a GAM smoother
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,y = lifeExp))
p + geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 3.6: Life expectancy vs GDP, using a GAM smoother and points
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,y = lifeExp))
p + geom_smooth() + geom_point()
Figure 3.7: Life expectancy vs GDP, using a linear model and points
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,y = lifeExp))
p + geom_point() + geom_smooth(method = "lm")
- Gross domestic product is not normally distributed across country years. Using a transformation of the x-axis would likely improve the visualization
Figure 3.8: Life expectancy vs GDP scatterplot, with a GAM smoother and a log scale on the x-axis
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,y = lifeExp))
p + geom_point() + geom_smooth(method = "gam") + scale_x_log10()
Figure 3.9: Life expectancy vs GDP scatterplot, with a GAM smoother and a log scale on the x-axis, with x-labels as dollar values
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,y = lifeExp))
p + geom_point() + geom_smooth(method = "gam") + scale_x_log10(labels = scales::dollar)
ggplot(data = gapminder,
mapping = aes(x = gdpPercap,y =lifeExp,color = continent)) +
geom_point() + scale_x_log10(labels = scales::dollar)
Figure 3.10: Same as above, but setting color independently of dataset variables.
p <- ggplot(data = gapminder,
mapping = aes(x = gdpPercap, y = lifeExp,
color = c("purple")))
p + geom_point() + geom_smooth(method = "loess") + scale_x_log10()
An aesthetic is a mapping of variables in your data to properties you can see on the graph. The aes() function is where the mappings are specified: where you can translate variables to graphical properties.
The code for section 3.10, in effect, creates a new variable for the data. The string “purple” is recycled for every row.
The aes() function is for mappings only. Do not use it to change properties to a particular value. If we want to set a property, we should do within each specific geom_ and outside the mapping = aes().
Figure 3.11: Setting the color attribute of the points directly
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(color = "purple") + geom_smooth(method = "loess") +
scale_x_log10(labels = scales::dollar)
Figure 3.12: Changing other arguments
p <- ggplot(gapminder,aes(x = gdpPercap, y = lifeExp))
p + geom_point(alpha = 0.3) + geom_smooth(color = "orange", se = FALSE,
size =1.5, method = "lm") +
scale_x_log10()
Figure 3.13: A more polished plt of Life Expectancy vs GDP
p <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp))
p + geom_point(alpha = 0.3) + ###Setting transparency of points
geom_smooth(method = "gam") + ##Setting method
scale_x_log10(labels = scales::dollar) +
labs(x = "GDP Per Capita in U$",
y = "Life Expectancy in Years",
title = "Economic Growth and Life Expectancy",
subtitle = "Data points are country years",
caption = "Source: Gapminder")
The labs() function controls the main labels of scales. Axis tick-marks is the responsability of the various scale_ functions.
Unless told otherwise, all geoms layered on top of the original plot object will inherit that object’s mapping.
Figure 3.14: Mapping the continent variable to the color aesthetic
p <- ggplot(data = gapminder,
mapping = aes(x = gdpPercap,
y = lifeExp,
color = continent))
p + geom_point() + geom_smooth(method = "loess") + scale_x_log10() +
theme(legend.position = "top")
- Whereas the color aesthetic affects the appearance of lines and points, fill is for the filled areas of bars, polygons and, also, the interior of smoother’s standard error ribbon.
Figure 3.15: Mapping the continent variable to the color and fill aesthetics
p <- ggplot(gapminder, aes(x = gdpPercap,
y = lifeExp,
color = continent,
fill = continent))
p + geom_point() + geom_smooth(method = "loess") + scale_x_log10()
By default, geoms inherit their mappings from the ggplot() function. We can change this by specifying different aesthetics for each geom(). We can use the same mapping = aes(…) expression as in the initial call to ggplot(), but now use it in the geom_ functions as well, specifying the mappings we want to apply to each one.
Mappings specified in the initial ggplot() function will carry through to all subsequent geoms.
Figure 3.16: Mapping aesthetics on a per geom basis. Color is mapped to continent for the points, but not for the smoother.
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap,
y = lifeExp))
p + geom_point(mapping = aes(color= continent)) +
geom_smooth(method = "loess") +
scale_x_log10()
- It is also possible to map continuous variables to the color aesthetic. When doing so, ggplot producesa gradient scale.
Figure 3.17; Mapping a continuous variable to color
p <- ggplot(data = gapminder,
mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = log(pop))) + scale_x_log10()
Figure 3.x1: Setting the size of output for each R markdown chunk
p <- ggplot(data = gapminder,
mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = log(pop))) + scale_x_log10()